Analyzing Graduation Project Ideas by using Machine Learning
Dublin Core
Title
Analyzing Graduation Project Ideas by using Machine Learning
Subject
Machine Learning
Text Classification
System Analyze
Graduation Project
Description
The graduation projects (GP) are important because it reflects the academic profile and achievement of the students. For many years’ graduation projects are done by the information technology department students. Most of these projects have great value, and some were published in scientific journals and international conferences. However, these projects are stored in an archive room haphazardly and there is a very small part of it is a set of electronic PDF files stored on hard disk, which wastes time and effort and cannot benefit from it. However, there is no system to classify and store these projects in a good way that can benefit from them. In this paper, we reviewed some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm, random forest (RF) algorithm, which can deal with an extremely small amount of dataset after comparing these algorithms based on accuracy. We choose the SVM algorithm to classify the projects. Besides, we will mention how to deal with a super small dataset and solve this problem.
Creator
Alharbi, Hajar A.
Alshaya, Hessa I.
Alsheail, Meshaiel M.
Koujan, Mukhlisah H.
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 23 (2021); pp. 136-147
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2021-12-08
Rights
Copyright (c) 2021 Hajar A. Alharbi, Hessa I. Alshaya, Meshaiel M. Alsheail, Mukhlisah H. Koujan
https://creativecommons.org/licenses/by/4.0
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Hajar Alharbi A. et al., Analyzing Graduation Project Ideas by using Machine Learning, International Association of Online Engineering (IAOE), Vienna, Austria, 2021, accessed November 6, 2024, https://igi.indrastra.com/items/show/2181